### Abstract

This paper addresses the problem of distributed learning under communication constraints, motivated by distributed signal processing in wireless sensor networks and data mining with distributed databases. After formalizing a general model for distributed learning, an algorithm for collaboratively training regularized kernel least-squares regression estimators is derived. Noting that the algorithm can be viewed as an application of successive orthogonal projection algorithms, its convergence properties are investigated and the statistical behavior of the estimator is discussed in a simplified theoretical setting.

Original language | English (US) |
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Title of host publication | 2006 IEEE Information Theory Workshop, ITW 2006 |

Pages | 332-336 |

Number of pages | 5 |

State | Published - Nov 21 2006 |

Event | 2006 IEEE Information Theory Workshop, ITW 2006 - Punta del Este, Uruguay Duration: Mar 13 2006 → Mar 17 2006 |

### Publication series

Name | 2006 IEEE Information Theory Workshop, ITW 2006 |
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### Other

Other | 2006 IEEE Information Theory Workshop, ITW 2006 |
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Country | Uruguay |

City | Punta del Este |

Period | 3/13/06 → 3/17/06 |

### All Science Journal Classification (ASJC) codes

- Engineering(all)

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## Cite this

Predd, J. B., Kulkarni, S. R., & Poor, H. V. (2006). Distributed kernel regression: An algorithm for training collaboratively. In

*2006 IEEE Information Theory Workshop, ITW 2006*(pp. 332-336). [1633840] (2006 IEEE Information Theory Workshop, ITW 2006).